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**AD-TEAM** · 11-26-2024, 01:47 PM

5.85 GB | 00:22:23 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English

Files Included :
001 Course content (167.24 MB)
002 Introduction to GANs (156.67 MB)
003 How GANs work (25.63 MB)
001 DCGAN - intuition (15.54 MB)
002 MNIST dataset (127.36 MB)
003 Building the generator (167.54 MB)
004 Building the discriminator (93.41 MB)
005 Loss (error) calculation (70.58 MB)
007 Training (68.42 MB)
008 Visualizing the results (55.21 MB)
010 WGAN - intuition 1 (65.69 MB)
011 WGAN - intuition 2 (29.92 MB)
012 WGAN-GP - intuition (12.08 MB)
013 Preparing the environment (33.11 MB)
014 Wassertein loss (73.98 MB)
015 Gradient penalty (96.86 MB)
016 Training 1 (100.49 MB)
017 Training 2 and visualization (109 MB)
001 cGAN - intuition (35.07 MB)
002 Pix2Pix - intuition (67.48 MB)
003 Map dataset (28.57 MB)
004 Preprocessing the images 1 (32.72 MB)
005 Preprocessing the images 2 (96.12 MB)
006 Loading the data (50.01 MB)
007 Building the generator 1 (109.77 MB)
008 Building the generator 2 (177.63 MB)
009 Building the generator 3 (62.55 MB)
010 Building the discriminator 1 (118.01 MB)
011 Building the discriminator 2 (33.54 MB)
012 Generating the images (53.11 MB)
013 Training 1 (90.57 MB)
014 Training 2 and results (220.88 MB)
015 Pretrained Pix2Pix with PyTorch (82.2 MB)
016 Facades dataset (16.97 MB)
017 Visualizing the results (50.18 MB)
018 Drawing to photo 1 (38.49 MB)
019 Drawing to photo 2 (70.83 MB)
020 Night to day (26.63 MB)
022 CycleGAN - intuition (41.64 MB)
024 Apples and orange dataset (36.86 MB)
025 Preprocessing (11.14 MB)
026 Loading the images (47.91 MB)
027 Generator and discriminator (102.45 MB)
028 Loss function (78.58 MB)
029 Optimizers and checkpoint (14.01 MB)
030 Training 1 (145.56 MB)
031 Training 2 and results (92.34 MB)
032 Pretrained CycleGAN with PyTorch (40.35 MB)
033 Horse to zebra (46.07 MB)
034 Style transfer (51.49 MB)
035 Van Gogh, Cezanne and Ukiyo-e styles (37.05 MB)
001 SRGAN - intuition (64.13 MB)
002 ESRGAN - intuition (23.98 MB)
003 Pretrained model (105.62 MB)
004 Testing images (22.1 MB)
005 Super resolution (46.95 MB)
006 Evaluating the results - PSNR (121.92 MB)
007 Improving the results (80.94 MB)
001 ProGAN - intuition (74.09 MB)
002 StyleGAN - intuition (26.24 MB)
003 Pretrained model (40.54 MB)
004 Generating images 1 (60.76 MB)
005 Generating images 2 (71.49 MB)
006 Generating images 3 (68.43 MB)
007 Interpolation (86.37 MB)
008 Other pretrained models (10.07 MB)
001 VQGAN + CLIP - intuition (60.36 MB)
003 Pretrained model (56.83 MB)
004 GAN settings (38.12 MB)
005 Visualizing the results (73.54 MB)
006 Results in videos (42.41 MB)
001 BigGAN - intuition (7.98 MB)
002 Pretrained model (38.14 MB)
003 GAN settings (69.97 MB)
004 Generating new images 1 (20.73 MB)
005 Generating new images 2 (141.96 MB)
006 GFP-GAN to restore old photos (7.93 MB)
007 Pretrained model (52.98 MB)
008 Photo restoration (134.88 MB)
009 Boundless for image extension (9.32 MB)
010 Processing the image (24.14 MB)
011 Visualizing the results (74.41 MB)
012 SimSwap for deepfake (3.85 MB)
013 Pretrained model (67.41 MB)
014 Face swap (64.78 MB)
001 Biological fundamentals (58.45 MB)
002 Single layer perceptron (43.23 MB)
003 Multilayer perceptron - sum and activation functions (24.32 MB)
004 Multilayer perceptron - error calculation (8.57 MB)
005 Gradient descent (20.39 MB)
006 Delta parameter (14.41 MB)
007 Updating weights with backpropagation (26.83 MB)
008 Bias, error, stochastic gradient descent, and more parameters (52.27 MB)
001 Introduction to convolutional neural networks (23.42 MB)
002 Convolutional operator (29.81 MB)
003 Pooling (9.35 MB)
004 Flattening (61.78 MB)
005 Dense neural network (19.2 MB)
001 Final remarks (2.57 MB)
002 BONUS (30.19 MB)]
Screenshot
[Image: UZxVCzjq_o.jpg]